Various systems and methods for image sequence processing can be used to study sequences of images. In some applications, such systems or methods are used to identify features within an image sequence and/or to segment an image based on such identified features. In some applications, one or more sequences of images are compared, contrasted, or related. In another application, features of one image in a sequence are used to predict features of another image of the same sequence. Other applications of image sequence processing are known in the art. Such processing can be applied to various image subjects.
One established application of image sequence processing is the processing of echocardiography sequences. Real-time 2D echocardiography (ultrasound imaging of the heart) is a well-established clinical tool for cardiovascular disease diagnosis. One area of interest is stress echocardiography where heart disease is assessed by imaging at rest and then again after a stress stimulus is applied to the subject that makes the heart beat faster and harder. Although a stressed heart moves very differently to when it is at rest, the motion of the two sequences is related and it is this relation that a clinician is trying to infer qualitatively. Currently, the analysis is carried out through visual assessment by expert clinicians but this is very subjective and can result in discrepancies when assessed by different clinicians. In order to overcome these issues, the analysis techniques should be carried out in a more automated (i.e. less subjective) and quantitative manner.
One step toward automating such analysis is to delineate important structures within the heart. For the purpose of stress echocardiography, clinicians often observe the endocardial border of the left ventricle of the heart as it can provide important measures such as left ventricle volume and ejection fraction. Despite the development of segmentation and tracking techniques for the heart at rest, there is little literature on the same for the stressed heart, although some contribution to the field has been made by Shekhar and Leung. E.g., U.S. patent application Ser. No. 12/371,111, V. Walimbe, M. Garcia, O. Lalude, J. Thomas, and R. Shekhar, “Quantitative real-time three-dimensional stress echocardiography: A preliminary investigation of feasibility and effectiveness,” J. Am. Soc. Echocardiography, 20(1), pp. 13-22, 2007; V. Zagrodsky, V. Walimbe, C. R. Castro-Pareja, J. X. Qin, J.-M. Song, and R. Shekhar, “Registration-assisted segmentation of real-time 3-d echocardiographic data using deformable models,” IEEE Trans. Med. Imag., 24(9), pp. 1089-1099, 2005; K. Y. Leung, M. van Stralen, A. Nemes, M. M. Voormolen, G. van Burken, M. L. Geleijnse, F. J. Ten Cate, J. H. Reiber, N. de Jong, A. F. van der Steen, and J. G. Bosch, “Sparse registration for three-dimensional stress echocardiography,” IEEE Trans. Med. Imag., 27(11), pp. 1568-1579, 2008. This limited progress in automating the analysis of stress echocardiography is because the motion of the heart under stress is much more difficult to model because of increased inter-subject variability and more complex dynamics in stress. Tracking algorithms that have been successfully applied to rest datasets cannot necessarily be applied directly to stress datasets as the image quality is poorer and the temporal resolution for the cardiac cycle is lower (around half that at rest). Optical flow based algorithms, for example, assume that the motion between frames is small and this is clearly violated in stress due to lower temporal resolutions. Another factor is that the shape variation throughout the frames from the end-diastolic (ED) to end-systolic (ES) is greater for stress than for rest data. An example of this variation can be seen in FIGS. 9 and 10, which show rest and stress frames in an echocardiogram from ED to ES, respectively.